Articles | Volume 23, issue 1
https://doi.org/10.5194/hess-23-447-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-23-447-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
25 Jan 2019
Research article |
| 25 Jan 2019
| 25 Jan 2019
Statistical approaches for identification of low-flow drivers: temporal aspects
Anne Fangmann
CORRESPONDING AUTHOR
Institute of Hydrology and Water Resources Management, Leibniz
University of Hannover, Germany
Uwe Haberlandt
Institute of Hydrology and Water Resources Management, Leibniz
University of Hannover, Germany
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Anne Bartens, Bora Shehu, and Uwe Haberlandt
Hydrol. Earth Syst. Sci., 28, 1687–1709, https://doi.org/10.5194/hess-28-1687-2024, https://doi.org/10.5194/hess-28-1687-2024, 2024
Short summary
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River flow data are often provided as mean daily flows (MDF), in which a lot of information is lost about the actual maximum flow or instantaneous peak flows (IPF) within a day. We investigate the error of using MDF instead of IPF and identify means to predict IPF when only MDF data are available. We find that the average ratio of daily flood peaks and volumes is a good predictor, which is easily and universally applicable and requires a minimum amount of data.
Anne Bartens and Uwe Haberlandt
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2021-466, https://doi.org/10.5194/hess-2021-466, 2021
Preprint withdrawn
Short summary
Short summary
River flow data is often provided as mean daily flow (MDF), in which a lot of information is lost about the actual maximum flow or instantaneous peak flow (IPF) within a day. We investigate the error of using MDFs instead of IPFs and identify means to predict IPFs when only MDF data is available. We find that the average ratio of daily flood peaks and volumes is a good predictor, which is easily and universally applicable and requires a minimum amount of data.
Golbarg Goshtasbpour and Uwe Haberlandt
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2024-177, https://doi.org/10.5194/hess-2024-177, 2024
Preprint under review for HESS
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We provide a method how to estimate extreme rainfall from radar observations. Extreme value statistic is applied for observed radar rainfall covering different areas from point size up to about 1000 km2. The rainfall extremes are supposed to be as higher as smaller the area is. This behaviour could not be confirmed by the radar observations. The reason is the limited single point sampling approach of the radar data. New multiple point sampling strategies are proposed to mitigate this problem.
Anne Bartens, Bora Shehu, and Uwe Haberlandt
Hydrol. Earth Syst. Sci., 28, 1687–1709, https://doi.org/10.5194/hess-28-1687-2024, https://doi.org/10.5194/hess-28-1687-2024, 2024
Short summary
Short summary
River flow data are often provided as mean daily flows (MDF), in which a lot of information is lost about the actual maximum flow or instantaneous peak flows (IPF) within a day. We investigate the error of using MDF instead of IPF and identify means to predict IPF when only MDF data are available. We find that the average ratio of daily flood peaks and volumes is a good predictor, which is easily and universally applicable and requires a minimum amount of data.
Ross Pidoto and Uwe Haberlandt
Hydrol. Earth Syst. Sci., 27, 3957–3975, https://doi.org/10.5194/hess-27-3957-2023, https://doi.org/10.5194/hess-27-3957-2023, 2023
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Long continuous time series of meteorological variables (i.e. rainfall, temperature) are required for the modelling of floods. Observed time series are generally too short or not available. Weather generators are models that reproduce observed weather time series. This study extends an existing station-based rainfall model into space by enforcing observed spatial rainfall characteristics. To model other variables (i.e. temperature) the model is then coupled to a simple resampling approach.
Bora Shehu and Uwe Haberlandt
Hydrol. Earth Syst. Sci., 27, 2075–2097, https://doi.org/10.5194/hess-27-2075-2023, https://doi.org/10.5194/hess-27-2075-2023, 2023
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Design rainfall volumes at different duration and frequencies are necessary for the planning of water-related systems and facilities. As the procedure for deriving these values is subjected to different sources of uncertainty, here we explore different methods to estimate how precise these values are for different duration, locations and frequencies in Germany. Combining local and spatial simulations, we estimate tolerance ranges from approx. 10–60% for design rainfall volumes in Germany.
Bora Shehu, Winfried Willems, Henrike Stockel, Luisa-Bianca Thiele, and Uwe Haberlandt
Hydrol. Earth Syst. Sci., 27, 1109–1132, https://doi.org/10.5194/hess-27-1109-2023, https://doi.org/10.5194/hess-27-1109-2023, 2023
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Rainfall volumes at varying duration and frequencies are required for many engineering water works. These design volumes have been provided by KOSTRA-DWD in Germany. However, a revision of the KOSTRA-DWD is required, in order to consider the recent state-of-the-art and additional data. For this purpose, in our study, we investigate different methods and data available to achieve the best procedure that will serve as a basis for the development of the new KOSTRA-DWD product.
Ross Pidoto, Nejc Bezak, Hannes Müller-Thomy, Bora Shehu, Ana Claudia Callau-Beyer, Katarina Zabret, and Uwe Haberlandt
Earth Surf. Dynam., 10, 851–863, https://doi.org/10.5194/esurf-10-851-2022, https://doi.org/10.5194/esurf-10-851-2022, 2022
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Erosion is a threat for soils with rainfall as the driving force. The annual rainfall erosivity factor quantifies rainfall impact by analysing high-resolution rainfall time series (~ 5 min). Due to a lack of measuring stations, alternatives for its estimation are analysed in this study. The best results are obtained for regionalisation of the erosivity factor itself. However, the identified minimum of 60-year time series length suggests using rainfall generators as in this study as well.
Bora Shehu and Uwe Haberlandt
Hydrol. Earth Syst. Sci., 26, 1631–1658, https://doi.org/10.5194/hess-26-1631-2022, https://doi.org/10.5194/hess-26-1631-2022, 2022
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In this paper we investigate whether similar storms behave similarly and whether the information obtained from past similar storms can improve storm nowcast based on radar data. Here a nearest-neighbour approach is employed to first identify similar storms and later to issue either a single or an ensemble nowcast based on k most similar past storms. The results indicate that the information obtained from similar storms can reduce the errors considerably, especially for convective storm nowcast.
Anne Bartens and Uwe Haberlandt
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2021-466, https://doi.org/10.5194/hess-2021-466, 2021
Preprint withdrawn
Short summary
Short summary
River flow data is often provided as mean daily flow (MDF), in which a lot of information is lost about the actual maximum flow or instantaneous peak flow (IPF) within a day. We investigate the error of using MDFs instead of IPFs and identify means to predict IPFs when only MDF data is available. We find that the average ratio of daily flood peaks and volumes is a good predictor, which is easily and universally applicable and requires a minimum amount of data.
Ehsan Rabiei, Uwe Haberlandt, Monika Sester, Daniel Fitzner, and Markus Wallner
Hydrol. Earth Syst. Sci., 20, 3907–3922, https://doi.org/10.5194/hess-20-3907-2016, https://doi.org/10.5194/hess-20-3907-2016, 2016
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The value of using moving cars for rainfall measurement purposes (RCs) was investigated with laboratory experiments by Rabiei et al. (2013). They analyzed the Hydreon and Xanonex optical sensors against different rainfall intensities. A continuous investigation of using RCs with the derived uncertainties from laboratory experiments for areal rainfall estimation as well as implementing the data in a hydrological model are addressed in this study.
Uwe Haberlandt and Christian Berndt
Proc. IAHS, 373, 81–85, https://doi.org/10.5194/piahs-373-81-2016, https://doi.org/10.5194/piahs-373-81-2016, 2016
U. Haberlandt and I. Radtke
Hydrol. Earth Syst. Sci., 18, 353–365, https://doi.org/10.5194/hess-18-353-2014, https://doi.org/10.5194/hess-18-353-2014, 2014
E. Rabiei, U. Haberlandt, M. Sester, and D. Fitzner
Hydrol. Earth Syst. Sci., 17, 4701–4712, https://doi.org/10.5194/hess-17-4701-2013, https://doi.org/10.5194/hess-17-4701-2013, 2013
Related subject area
Subject: Catchment hydrology | Techniques and Approaches: Modelling approaches
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Adam Griffin, Alison L. Kay, Paul Sayers, Victoria Bell, Elizabeth Stewart, and Sam Carr
Hydrol. Earth Syst. Sci., 28, 2635–2650, https://doi.org/10.5194/hess-28-2635-2024, https://doi.org/10.5194/hess-28-2635-2024, 2024
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Widespread flooding is a major problem in the UK and is greatly affected by climate change and land-use change. To look at how widespread flooding changes in the future, climate model data (UKCP18) were used with a hydrological model (Grid-to-Grid) across the UK, and 14 400 events were identified between two time slices: 1980–2010 and 2050–2080. There was a strong increase in the number of winter events in the future time slice and in the peak return periods.
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Floods often take communities by surprise, as they are often considered virtually
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Peter Reichert, Kai Ma, Marvin Höge, Fabrizio Fenicia, Marco Baity-Jesi, Dapeng Feng, and Chaopeng Shen
Hydrol. Earth Syst. Sci., 28, 2505–2529, https://doi.org/10.5194/hess-28-2505-2024, https://doi.org/10.5194/hess-28-2505-2024, 2024
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We compared the predicted change in catchment outlet discharge to precipitation and temperature change for conceptual and machine learning hydrological models. We found that machine learning models, despite providing excellent fit and prediction capabilities, can be unreliable regarding the prediction of the effect of temperature change for low-elevation catchments. This indicates the need for caution when applying them for the prediction of the effect of climate change.
Nicolás Álamos, Camila Alvarez-Garreton, Ariel Muñoz, and Álvaro González-Reyes
Hydrol. Earth Syst. Sci., 28, 2483–2503, https://doi.org/10.5194/hess-28-2483-2024, https://doi.org/10.5194/hess-28-2483-2024, 2024
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In this study, we assess the effects of climate and water use on streamflow reductions and drought intensification during the last 3 decades in central Chile. We address this by contrasting streamflow observations with near-natural streamflow simulations. We conclude that while the lack of precipitation dominates streamflow reductions in the megadrought, water uses have not diminished during this time, causing a worsening of the hydrological drought conditions and maladaptation conditions.
Fengjing Liu, Martha H. Conklin, and Glenn D. Shaw
Hydrol. Earth Syst. Sci., 28, 2239–2258, https://doi.org/10.5194/hess-28-2239-2024, https://doi.org/10.5194/hess-28-2239-2024, 2024
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Mountain snowpack has been declining and more precipitation falls as rain than snow. Using stable isotopes, we found flows and flow duration in Yosemite Creek are most sensitive to climate warming due to strong evaporation of waterfalls, potentially lengthening the dry-up period of waterfalls in summer and negatively affecting tourism. Groundwater recharge in Yosemite Valley is primarily from the upper snow–rain transition (2000–2500 m) and very vulnerable to a reduction in the snow–rain ratio.
Qiutong Yu, Bryan A. Tolson, Hongren Shen, Ming Han, Juliane Mai, and Jimmy Lin
Hydrol. Earth Syst. Sci., 28, 2107–2122, https://doi.org/10.5194/hess-28-2107-2024, https://doi.org/10.5194/hess-28-2107-2024, 2024
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It is challenging to incorporate input variables' spatial distribution information when implementing long short-term memory (LSTM) models for streamflow prediction. This work presents a novel hybrid modelling approach to predict streamflow while accounting for spatial variability. We evaluated the performance against lumped LSTM predictions in 224 basins across the Great Lakes region in North America. This approach shows promise for predicting streamflow in large, ungauged basin.
Marcus Buechel, Louise Slater, and Simon Dadson
Hydrol. Earth Syst. Sci., 28, 2081–2105, https://doi.org/10.5194/hess-28-2081-2024, https://doi.org/10.5194/hess-28-2081-2024, 2024
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Afforestation has been proposed internationally, but the hydrological implications of such large increases in the spatial extent of woodland are not fully understood. In this study, we use a land surface model to simulate hydrology across Great Britain with realistic afforestation scenarios and potential climate changes. Countrywide afforestation minimally influences hydrology, when compared to climate change, and reduces low streamflow whilst not lowering the highest flows.
Qian Zhu, Xiaodong Qin, Dongyang Zhou, Tiantian Yang, and Xinyi Song
Hydrol. Earth Syst. Sci., 28, 1665–1686, https://doi.org/10.5194/hess-28-1665-2024, https://doi.org/10.5194/hess-28-1665-2024, 2024
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Input data, model and calibration strategy can affect the accuracy of flood event simulation and prediction. Satellite-based precipitation with different spatiotemporal resolutions is an important input source. Data-driven models are sometimes proven to be more accurate than hydrological models. Event-based calibration and conventional strategy are two options adopted for flood simulation. This study targets the three concerns for accurate flood event simulation and prediction.
Fabio Ciulla and Charuleka Varadharajan
Hydrol. Earth Syst. Sci., 28, 1617–1651, https://doi.org/10.5194/hess-28-1617-2024, https://doi.org/10.5194/hess-28-1617-2024, 2024
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We present a new method based on network science for unsupervised classification of large datasets and apply it to classify 9067 US catchments and 274 biophysical traits at multiple scales. We find that our trait-based approach produces catchment classes with distinct streamflow behavior and that spatial patterns emerge amongst pristine and human-impacted catchments. This method can be widely used beyond hydrology to identify patterns, reduce trait redundancy, and select representative sites.
Cyril Thébault, Charles Perrin, Vazken Andréassian, Guillaume Thirel, Sébastien Legrand, and Olivier Delaigue
Hydrol. Earth Syst. Sci., 28, 1539–1566, https://doi.org/10.5194/hess-28-1539-2024, https://doi.org/10.5194/hess-28-1539-2024, 2024
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Streamflow forecasting is useful for many applications, ranging from population safety (e.g. floods) to water resource management (e.g. agriculture or hydropower). To this end, hydrological models must be optimized. However, a model is inherently wrong. This study aims to analyse the contribution of a multi-model approach within a variable spatial framework to improve streamflow simulations. The underlying idea is to take advantage of the strength of each modelling framework tested.
Lele Shu, Xiaodong Li, Yan Chang, Xianhong Meng, Hao Chen, Yuan Qi, Hongwei Wang, Zhaoguo Li, and Shihua Lyu
Hydrol. Earth Syst. Sci., 28, 1477–1491, https://doi.org/10.5194/hess-28-1477-2024, https://doi.org/10.5194/hess-28-1477-2024, 2024
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We developed a new model to better understand how water moves in a lake basin. Our model improves upon previous methods by accurately capturing the complexity of water movement, both on the surface and subsurface. Our model, tested using data from China's Qinghai Lake, accurately replicates complex water movements and identifies contributing factors of the lake's water balance. The findings provide a robust tool for predicting hydrological processes, aiding water resource planning.
Ricardo Mantilla, Morgan Fonley, and Nicolás Velásquez
Hydrol. Earth Syst. Sci., 28, 1373–1382, https://doi.org/10.5194/hess-28-1373-2024, https://doi.org/10.5194/hess-28-1373-2024, 2024
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Hydrologists strive to “Be right for the right reasons” when modeling the hydrologic cycle; however, the datasets available to validate hydrological models are sparse, and in many cases, they comprise streamflow observations at the outlets of large catchments. In this work, we show that matching streamflow observations at the outlet of a large basin is not a reliable indicator of a correct description of the small-scale runoff processes.
Lillian M. McGill, E. Ashley Steel, and Aimee H. Fullerton
Hydrol. Earth Syst. Sci., 28, 1351–1371, https://doi.org/10.5194/hess-28-1351-2024, https://doi.org/10.5194/hess-28-1351-2024, 2024
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This study examines the relationship between air and river temperatures in Washington's Snoqualmie and Wenatchee basins. We used classification and regression approaches to show that the sensitivity of river temperature to air temperature is variable across basins and controlled largely by geology and snowmelt. Findings can be used to inform strategies for river basin restoration and conservation, such as identifying climate-insensitive areas of the basin that should be preserved and protected.
Stephanie R. Clark, Julien Lerat, Jean-Michel Perraud, and Peter Fitch
Hydrol. Earth Syst. Sci., 28, 1191–1213, https://doi.org/10.5194/hess-28-1191-2024, https://doi.org/10.5194/hess-28-1191-2024, 2024
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To determine if deep learning models are in general a viable alternative to traditional hydrologic modelling techniques in Australian catchments, a comparison of river–runoff predictions is made between traditional conceptual models and deep learning models in almost 500 catchments spread over the continent. It is found that the deep learning models match or outperform the traditional models in over two-thirds of the river catchments, indicating feasibility in a wide variety of conditions.
Dipti Tiwari, Mélanie Trudel, and Robert Leconte
Hydrol. Earth Syst. Sci., 28, 1127–1146, https://doi.org/10.5194/hess-28-1127-2024, https://doi.org/10.5194/hess-28-1127-2024, 2024
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Calibrating hydrological models with multi-objective functions enhances model robustness. By using spatially distributed snow information in the calibration, the model performance can be enhanced without compromising the outputs. In this study the HYDROTEL model was calibrated in seven different experiments, incorporating the SPAEF (spatial efficiency) metric alongside Nash–Sutcliffe efficiency (NSE) and root-mean-square error (RMSE), with the aim of identifying the optimal calibration strategy.
Luis Andres De la Fuente, Mohammad Reza Ehsani, Hoshin Vijai Gupta, and Laura Elizabeth Condon
Hydrol. Earth Syst. Sci., 28, 945–971, https://doi.org/10.5194/hess-28-945-2024, https://doi.org/10.5194/hess-28-945-2024, 2024
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Long short-term memory (LSTM) is a widely used machine-learning model in hydrology, but it is difficult to extract knowledge from it. We propose HydroLSTM, which represents processes like a hydrological reservoir. Models based on HydroLSTM perform similarly to LSTM while requiring fewer cell states. The learned parameters are informative about the dominant hydrology of a catchment. Our results show how parsimony and hydrological knowledge extraction can be achieved by using the new structure.
Louise Mimeau, Annika Künne, Flora Branger, Sven Kralisch, Alexandre Devers, and Jean-Philippe Vidal
Hydrol. Earth Syst. Sci., 28, 851–871, https://doi.org/10.5194/hess-28-851-2024, https://doi.org/10.5194/hess-28-851-2024, 2024
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Modelling flow intermittence is essential for predicting the future evolution of drying in river networks and better understanding the ecological and socio-economic impacts. However, modelling flow intermittence is challenging, and observed data on temporary rivers are scarce. This study presents a new modelling approach for predicting flow intermittence in river networks and shows that combining different sources of observed data reduces the model uncertainty.
Elena Macdonald, Bruno Merz, Björn Guse, Viet Dung Nguyen, Xiaoxiang Guan, and Sergiy Vorogushyn
Hydrol. Earth Syst. Sci., 28, 833–850, https://doi.org/10.5194/hess-28-833-2024, https://doi.org/10.5194/hess-28-833-2024, 2024
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In some rivers, the occurrence of extreme flood events is more likely than in other rivers – they have heavy-tailed distributions. We find that threshold processes in the runoff generation lead to such a relatively high occurrence probability of extremes. Further, we find that beyond a certain return period, i.e. for rare events, rainfall is often the dominant control compared to runoff generation. Our results can help to improve the estimation of the occurrence probability of extreme floods.
Claire Kouba and Thomas Harter
Hydrol. Earth Syst. Sci., 28, 691–718, https://doi.org/10.5194/hess-28-691-2024, https://doi.org/10.5194/hess-28-691-2024, 2024
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In some watersheds, the severity of the dry season has a large impact on aquatic ecosystems. In this study, we design a way to predict, 5–6 months in advance, how severe the dry season will be in a rural watershed in northern California. This early warning can support seasonal adaptive management. To predict these two values, we assess data about snow, rain, groundwater, and river flows. We find that maximum snowpack and total wet season rainfall best predict dry season severity.
Yi Nan and Fuqiang Tian
Hydrol. Earth Syst. Sci., 28, 669–689, https://doi.org/10.5194/hess-28-669-2024, https://doi.org/10.5194/hess-28-669-2024, 2024
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This paper utilized a tracer-aided model validated by multiple datasets in a large mountainous basin on the Tibetan Plateau to analyze hydrological sensitivity to climate change. The spatial pattern of the local hydrological sensitivities and the influence factors were analyzed in particular. The main finding of this paper is that the local hydrological sensitivity in mountainous basins is determined by the relationship between the glacier area ratio and the mean annual precipitation.
Michael J. Vlah, Matthew R. V. Ross, Spencer Rhea, and Emily S. Bernhardt
Hydrol. Earth Syst. Sci., 28, 545–573, https://doi.org/10.5194/hess-28-545-2024, https://doi.org/10.5194/hess-28-545-2024, 2024
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Virtual stream gauging enables continuous streamflow estimation where a gauge might be difficult or impractical to install. We reconstructed flow at 27 gauges of the National Ecological Observatory Network (NEON), informing ~199 site-months of missing data in the official record and improving that accuracy of official estimates at 11 sites. This study shows that machine learning, but also routine regression methods, can be used to supplement existing gauge networks and reduce monitoring costs.
Sungwook Wi and Scott Steinschneider
Hydrol. Earth Syst. Sci., 28, 479–503, https://doi.org/10.5194/hess-28-479-2024, https://doi.org/10.5194/hess-28-479-2024, 2024
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We investigate whether deep learning (DL) models can produce physically plausible streamflow projections under climate change. We address this question by focusing on modeled responses to increases in temperature and potential evapotranspiration and by employing three DL and three process-based hydrological models. The results suggest that physical constraints regarding model architecture and input are necessary to promote the physical realism of DL hydrological projections under climate change.
Guillaume Evin, Matthieu Le Lay, Catherine Fouchier, David Penot, Francois Colleoni, Alexandre Mas, Pierre-André Garambois, and Olivier Laurantin
Hydrol. Earth Syst. Sci., 28, 261–281, https://doi.org/10.5194/hess-28-261-2024, https://doi.org/10.5194/hess-28-261-2024, 2024
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Hydrological modelling of mountainous catchments is challenging for many reasons, the main one being the temporal and spatial representation of precipitation forcings. This study presents an evaluation of the hydrological modelling of 55 small mountainous catchments of the northern French Alps, focusing on the influence of the type of precipitation reanalyses used as inputs. These evaluations emphasize the added value of radar measurements, in particular for the reproduction of flood events.
Maik Renner and Corina Hauffe
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2024-6, https://doi.org/10.5194/hess-2024-6, 2024
Revised manuscript accepted for HESS
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Climate and land-surface conditions influence the availability of fresh water resources. Their impact is quantified with data of 71 catchments in Saxony/Germany, for which distinct signatures in the joint water and energy budgets are found: (i) past forest dieback caused a decrease and subsequent recovery of evapotranspiration in the affected regions, and (ii) the recent shift towards higher aridity imposed a large decline in runoff, that has not been seen in the observation records before.
Lena Katharina Schmidt, Till Francke, Peter Martin Grosse, and Axel Bronstert
Hydrol. Earth Syst. Sci., 28, 139–161, https://doi.org/10.5194/hess-28-139-2024, https://doi.org/10.5194/hess-28-139-2024, 2024
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How suspended sediment export from glacierized high-alpine areas responds to future climate change is hardly assessable as many interacting processes are involved, and appropriate physical models are lacking. We present the first study, to our knowledge, exploring machine learning to project sediment export until 2100 in two high-alpine catchments. We find that uncertainties due to methodological limitations are small until 2070. Negative trends imply that peak sediment may have already passed.
Huy Dang and Yadu Pokhrel
EGUsphere, https://doi.org/10.5194/egusphere-2023-3158, https://doi.org/10.5194/egusphere-2023-3158, 2024
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By examining basin-wide simulations of the river regime over 83 years with and without dams, we present evidence that climate variation was a key driver of hydrologic variabilities in the Mekong River Basin (MRB) over the long-term; however, dams have largely altered the seasonality of Mekong’s flow regime and annual flooding patterns at major downstream areas in recent years. These findings could help rethink the planning of future dams and water resource management in the MRB.
Salam A. Abbas, Ryan T. Bailey, Jeremy T. White, Jeffrey G. Arnold, Michael J. White, Natalja Čerkasova, and Jungang Gao
Hydrol. Earth Syst. Sci., 28, 21–48, https://doi.org/10.5194/hess-28-21-2024, https://doi.org/10.5194/hess-28-21-2024, 2024
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Research highlights.
1. Implemented groundwater module (gwflow) into SWAT+ for four watersheds with different unique hydrologic features across the United States.
2. Presented methods for sensitivity analysis, uncertainty analysis and parameter estimation for coupled models.
3. Sensitivity analysis for streamflow and groundwater head conducted using Morris method.
4. Uncertainty analysis and parameter estimation performed using an iterative ensemble smoother within the PEST framework.
Shima Azimi, Christian Massari, Giuseppe Formetta, Silvia Barbetta, Alberto Tazioli, Davide Fronzi, Sara Modanesi, Angelica Tarpanelli, and Riccardo Rigon
Hydrol. Earth Syst. Sci., 27, 4485–4503, https://doi.org/10.5194/hess-27-4485-2023, https://doi.org/10.5194/hess-27-4485-2023, 2023
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We analyzed the water budget of nested karst catchments using simple methods and modeling. By utilizing the available data on precipitation and discharge, we were able to determine the response lag-time by adopting new techniques. Additionally, we modeled snow cover dynamics and evapotranspiration with the use of Earth observations, providing a concise overview of the water budget for the basin and its subbasins. We have made the data, models, and workflows accessible for further study.
Yuhang Zhang, Aizhong Ye, Bita Analui, Phu Nguyen, Soroosh Sorooshian, Kuolin Hsu, and Yuxuan Wang
Hydrol. Earth Syst. Sci., 27, 4529–4550, https://doi.org/10.5194/hess-27-4529-2023, https://doi.org/10.5194/hess-27-4529-2023, 2023
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Our study shows that while the quantile regression forest (QRF) and countable mixtures of asymmetric Laplacians long short-term memory (CMAL-LSTM) models demonstrate similar proficiency in multipoint probabilistic predictions, QRF excels in smaller watersheds and CMAL-LSTM in larger ones. CMAL-LSTM performs better in single-point deterministic predictions, whereas QRF model is more efficient overall.
Zehua Chang, Hongkai Gao, Leilei Yong, Kang Wang, Rensheng Chen, Chuntan Han, Otgonbayar Demberel, Batsuren Dorjsuren, Shugui Hou, and Zheng Duan
EGUsphere, https://doi.org/10.5194/egusphere-2023-3043, https://doi.org/10.5194/egusphere-2023-3043, 2023
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An integrated cryospheric-hydrologic model, FLEX-Cryo, was developed, considered glaciers, snow cover, frozen soil, and their dynamic impacts on hydrology. We utilized the model to simulate the future changes in cryosphere and hydrology in Hulu catchment. Our projections showed that the two glaciers will melt out around 2050; snow cover will reduce; and permafrost will degrade. For hydrology, runoff will decrease after glacier melt out; and permafrost degradation will increase baseflow.
Léo C. P. Martin, Sebastian Westermann, Michele Magni, Fanny Brun, Joel Fiddes, Yanbin Lei, Philip Kraaijenbrink, Tamara Mathys, Moritz Langer, Simon Allen, and Walter W. Immerzeel
Hydrol. Earth Syst. Sci., 27, 4409–4436, https://doi.org/10.5194/hess-27-4409-2023, https://doi.org/10.5194/hess-27-4409-2023, 2023
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Across the Tibetan Plateau, many large lakes have been changing level during the last decades as a response to climate change. In high-mountain environments, water fluxes from the land to the lakes are linked to the ground temperature of the land and to the energy fluxes between the ground and the atmosphere, which are modified by climate change. With a numerical model, we test how these water and energy fluxes have changed over the last decades and how they influence the lake level variations.
Diego Araya, Pablo A. Mendoza, Eduardo Muñoz-Castro, and James McPhee
Hydrol. Earth Syst. Sci., 27, 4385–4408, https://doi.org/10.5194/hess-27-4385-2023, https://doi.org/10.5194/hess-27-4385-2023, 2023
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Dynamical systems are used by many agencies worldwide to produce seasonal streamflow forecasts, which are critical for decision-making. Such systems rely on hydrology models, which contain parameters that are typically estimated using a target performance metric (i.e., objective function). This study explores the effects of this decision across mountainous basins in Chile, illustrating tradeoffs between seasonal forecast quality and the models' capability to simulate streamflow characteristics.
Pamela E. Tetford and Joseph R. Desloges
Hydrol. Earth Syst. Sci., 27, 3977–3998, https://doi.org/10.5194/hess-27-3977-2023, https://doi.org/10.5194/hess-27-3977-2023, 2023
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An efficient regional flood frequency model relates drainage area to discharge, with a major assumption of similar basin conditions. In a landscape with variable glacial deposits and land use, we characterize varying hydrological function using 28 explanatory variables. We demonstrate that (1) a heterogeneous landscape requires objective model selection criteria to optimize the fit of flow data, and (2) incorporating land use as a predictor variable improves the drainage area to discharge model.
Yalan Song, Wouter J. M. Knoben, Martyn P. Clark, Dapeng Feng, Kathryn E. Lawson, and Chaopeng Shen
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2023-258, https://doi.org/10.5194/hess-2023-258, 2023
Revised manuscript accepted for HESS
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Wouldn't it be nice to have both the accuracy of neural networks (NNs) and the interpretability of process-based models (PBMs)? Differentiable modeling gives you the best of both worlds by connecting NNs with PBMs. However, there was previously a major issue that iterative solution schemes would run into memory use trouble. This paper presents an operator called adjoint, which liberates all the iterative solvers. This is the first time adjoint is applied to large-scale hydrologic modeling.
Ana Ramos Oliveira, Tiago Brito Ramos, Lígia Pinto, and Ramiro Neves
Hydrol. Earth Syst. Sci., 27, 3875–3893, https://doi.org/10.5194/hess-27-3875-2023, https://doi.org/10.5194/hess-27-3875-2023, 2023
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This paper intends to demonstrate the adequacy of a hybrid solution to overcome the difficulties related to the incorporation of human behavior when modeling hydrological processes. Two models were implemented, one to estimate the outflow of a reservoir and the other to simulate the hydrological processes of the watershed. With both models feeding each other, results show that the proposed approach significantly improved the streamflow estimation downstream of the reservoir.
Yongshin Lee, Francesca Pianosi, Andres Peñuela, and Miguel Angel Rico-Ramirez
EGUsphere, https://doi.org/10.5194/egusphere-2023-2169, https://doi.org/10.5194/egusphere-2023-2169, 2023
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Following recent advancements in weather prediction technology, we explored how seasonal weather forecasts (one or more months ahead) could benefit practical water management in South Korea. Our findings highlight that using seasonal weather forecasts for predicting flow patterns 1 to 3 months ahead is effective, especially during dry years. This suggest that seasonal weather forecasts can be helpful in improving the management of water resources.
Zhihua He, Kevin Shook, Christopher Spence, John W. Pomeroy, and Colin Whitfield
Hydrol. Earth Syst. Sci., 27, 3525–3546, https://doi.org/10.5194/hess-27-3525-2023, https://doi.org/10.5194/hess-27-3525-2023, 2023
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This study evaluated the impacts of climate change on snowmelt, soil moisture, and streamflow over the Canadian Prairies. The entire prairie region was divided into seven basin types. We found strong variations of hydrological sensitivity to precipitation and temperature changes in different land covers and basins, which suggests that different water management and adaptation methods are needed to address enhanced water stress due to expected climate change in different regions of the prairies.
Nicolás Cortés-Salazar, Nicolás Vásquez, Naoki Mizukami, Pablo A. Mendoza, and Ximena Vargas
Hydrol. Earth Syst. Sci., 27, 3505–3524, https://doi.org/10.5194/hess-27-3505-2023, https://doi.org/10.5194/hess-27-3505-2023, 2023
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This paper shows how important river models can be for water resource applications that involve hydrological models and, in particular, parameter calibration. To this end, we conduct numerical experiments in a pilot basin using a combination of hydrologic model simulations obtained from a large sample of parameter sets and different routing methods. We find that routing can affect streamflow simulations, even at monthly time steps; the choice of parameters; and relevant streamflow metrics.
Dung Trung Vu, Thanh Duc Dang, Francesca Pianosi, and Stefano Galelli
Hydrol. Earth Syst. Sci., 27, 3485–3504, https://doi.org/10.5194/hess-27-3485-2023, https://doi.org/10.5194/hess-27-3485-2023, 2023
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The calibration of hydrological models over extensive spatial domains is often challenged by the lack of data on river discharge and the operations of hydraulic infrastructures. Here, we use satellite data to address the lack of data that could unintentionally bias the calibration process. Our study is underpinned by a computational framework that quantifies this bias and provides a safe approach to the calibration of models in poorly gauged and heavily regulated basins.
Francesco Fatone, Bartosz Szeląg, Przemysław Kowal, Arthur McGarity, Adam Kiczko, Grzegorz Wałek, Ewa Wojciechowska, Michał Stachura, and Nicolas Caradot
Hydrol. Earth Syst. Sci., 27, 3329–3349, https://doi.org/10.5194/hess-27-3329-2023, https://doi.org/10.5194/hess-27-3329-2023, 2023
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A novel methodology for the development of a stormwater network performance simulator including advanced risk assessment was proposed. The applied tool enables the analysis of the influence of spatial variability in catchment and stormwater network characteristics on the relation between (SWMM) model parameters and specific flood volume, as an alternative approach to mechanistic models. The proposed method can be used at the stage of catchment model development and spatial planning management.
Olivier Delaigue, Pierre Brigode, Guillaume Thirel, and Laurent Coron
Hydrol. Earth Syst. Sci., 27, 3293–3327, https://doi.org/10.5194/hess-27-3293-2023, https://doi.org/10.5194/hess-27-3293-2023, 2023
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Teaching hydrological modeling is an important, but difficult, matter. It requires appropriate tools and teaching material. In this article, we present the airGRteaching package, which is an open-source software tool relying on widely used hydrological models. This tool proposes an interface and numerous hydrological modeling exercises representing a wide range of hydrological applications. We show how this tool can be applied to simple but real-life cases.
Florian Willkofer, Raul Roger Wood, and Ralf Ludwig
EGUsphere, https://doi.org/10.5194/egusphere-2023-2019, https://doi.org/10.5194/egusphere-2023-2019, 2023
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Severe flood events pose threat to riverine areas, yet robust estimates about the dynamics of these events in the future due to climate change are rarely available. Hence, this study uses and benefits from data from a RCM SMILE to drive a high-resolution hydrological model for 98 catchments of the Hydrological Bavaria to exploit the large database to derive robust values for the 100-year flood events. Results indicate an increase in frequency and intensity for most catchments in the future.
Mariam Khanam, Giulia Sofia, and Emmanouil N. Anagnostou
EGUsphere, https://doi.org/10.5194/egusphere-2023-1969, https://doi.org/10.5194/egusphere-2023-1969, 2023
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Due to climate change, flooding is expected to become more frequent globally in the coming decades. Locally, storm-induced channel geometry changes can drastically affect flood hazards, yet rivers are mostly treated as static elements in flood studies. This study tried to gain an understanding of the effects of major storm events on future flood hazards, promoting a framework for incorporating channel conveyance adjustments into flood hazard assessment.
Siyuan Wang, Markus Hrachowitz, Gerrit Schoups, and Christine Stumpp
Hydrol. Earth Syst. Sci., 27, 3083–3114, https://doi.org/10.5194/hess-27-3083-2023, https://doi.org/10.5194/hess-27-3083-2023, 2023
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This study shows that previously reported underestimations of water ages are most likely not due to the use of seasonally variable tracers. Rather, these underestimations can be largely attributed to the choices of model approaches which rely on assumptions not frequently met in catchment hydrology. We therefore strongly advocate avoiding the use of this model type in combination with seasonally variable tracers and instead adopting StorAge Selection (SAS)-based or comparable model formulations.
Louise Akemi Kuana, Arlan Scortegagna Almeida, Emilio Graciliano Ferreira Mercuri, and Steffen Manfred Noe
EGUsphere, https://doi.org/10.5194/egusphere-2023-1755, https://doi.org/10.5194/egusphere-2023-1755, 2023
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The authors compared different regionalization methods for river flow prediction in watersheds with few data. We collected data on precipitation, evapotranspiration, river flow, and geographical and climatic factors for 126 catchments in the Paraná state, Brazil. The regionalization method based on physiographic-climatic similarity showed to be the most robust for predicting daily and Q95 reference flow. We also found patterns in data, grouping locations based on similarities.
Arianna Borriero, Rohini Kumar, Tam V. Nguyen, Jan H. Fleckenstein, and Stefanie R. Lutz
Hydrol. Earth Syst. Sci., 27, 2989–3004, https://doi.org/10.5194/hess-27-2989-2023, https://doi.org/10.5194/hess-27-2989-2023, 2023
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We analyzed the uncertainty of the water transit time distribution (TTD) arising from model input (interpolated tracer data) and structure (StorAge Selection, SAS, functions). We found that uncertainty was mainly associated with temporal interpolation, choice of SAS function, nonspatial interpolation, and low-flow conditions. It is important to characterize the specific uncertainty sources and their combined effects on TTD, as this has relevant implications for both water quantity and quality.
Yves Tramblay, Patrick Arnaud, Guillaume Artigue, Michel Lang, Emmanuel Paquet, Luc Neppel, and Eric Sauquet
Hydrol. Earth Syst. Sci., 27, 2973–2987, https://doi.org/10.5194/hess-27-2973-2023, https://doi.org/10.5194/hess-27-2973-2023, 2023
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Mediterranean floods are causing major damage, and recent studies have shown that, despite the increase in intense rainfall, there has been no increase in river floods. This study reveals that the seasonality of floods changed in the Mediterranean Basin during 1959–2021. There was also an increased frequency of floods linked to short episodes of intense rain, associated with a decrease in soil moisture. These changes need to be taken into consideration to adapt flood warning systems.
Yanfeng Wu, Jingxuan Sun, Boting Hu, Y. Jun Xu, Alain N. Rousseau, and Guangxin Zhang
Hydrol. Earth Syst. Sci., 27, 2725–2745, https://doi.org/10.5194/hess-27-2725-2023, https://doi.org/10.5194/hess-27-2725-2023, 2023
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Reservoirs and wetlands are important regulators of watershed hydrology, which should be considered when projecting floods and droughts. We first coupled wetlands and reservoir operations into a semi-spatially-explicit hydrological model and then applied it in a case study involving a large river basin in northeast China. We found that, overall, the risk of future floods and droughts will increase further even under the combined influence of reservoirs and wetlands.
Cited articles
Akaike, H.: New Look at Statistical-Model Identification, IEEE T. Automat.
Contr., 19, 716–723, https://doi.org/10.1109/Tac.1974.1100705, 1974.
Coles, S.: An introduction to statistical modeling of extreme values,
Springer, London, 2001.
Dai, A. G.: Increasing drought under global warming in observations and
models, Nat. Clim. Change, 3, 52–58, https://doi.org/10.1038/Nclimate1633, 2013.
de Wit, M. J. M., van den Hurk, B., Warmerdam, P. M. M., Torfs, P. J. J. F.,
Roulin, E., and van Deursen, W. P. A.: Impact of climate change on low-flows
in the river Meuse, Climatic Change, 82, 351–372,
https://doi.org/10.1007/s10584-006-9195-2, 2007.
Fangmann, A.: Low flow prediction in time and space: An adaptive statistical
scheme for regional climate change impact assessment, Mitteilungen, Heft 106,
Inst. of Hydrology, Leibniz University of Hannover, Hannover, 160 pp., 2017.
Fangmann, A., Belli, A., and Haberlandt, U.: Trends in beobachteten
Abflusszeitreihen in Niedersachsen, Hydrol. Wasserbewirts., 57, 196–205,
https://doi.org/10.5675/HyWa_2013,5_1, 2013.
Forzieri, G., Feyen, L., Rojas, R., Flörke, M., Wimmer, F., and Bianchi,
A.: Ensemble projections of future streamflow droughts in Europe, Hydrol.
Earth Syst. Sci., 18, 85–108, https://doi.org/10.5194/hess-18-85-2014, 2014.
Gosling, S. N., Zaherpour, J., Mount, N. J., Hattermann, F. F., Dankers, R.,
Arheimer, B., Breuer, L., Ding, J., Haddeland, I., Kumar, R., Kundu, D., Liu,
J. G., van Griensven, A., Veldkamp, T. I. E., Vetter, T., Wang, X. Y., and
Zhang, X. X.: A comparison of changes in river runoff from multiple global
and catchment-scale hydrological models under global warming scenarios of
1 ∘C, 2 ∘C and 3 ∘C, Climatic Change, 141,
577–595, https://doi.org/10.1007/s10584-016-1773-3, 2017.
Haslinger, K., Koffler, D., Schöner, W., and Laaha, G.: Exploring the
link between meteorological drought and streamflow, Effects of
climate-catchment interaction, Water Resour. Res., 50, 2468–2487,
https://doi.org/10.1002/2013WR015051, 2014.
Laaha, G., Parajka, J., Viglione, A., Koffler, D., Haslinger, K., Schöner,
W., Zehetgruber, J., and Blöschl, G.: A three-pillar approach to assessing
climate impacts on low flows, Hydrol. Earth Syst. Sci., 20, 3967–3985,
https://doi.org/10.5194/hess-20-3967-2016, 2016.
Lindstrom, G., Johansson, B., Persson, M., Gardelin, M., and Bergstrom, S.:
Development and test of the distributed HBV-96 hydrological model, J.
Hydrol., 201, 272–288, https://doi.org/10.1016/S0022-1694(97)00041-3, 1997.
Liu, D. D., Guo, S. L., Lian, Y. Q., Xiong, L. H., and Chen, X. H.:
Climate-informed low-flow frequency analysis using nonstationary modelling,
Hydrol. Process., 29, 2112–2124, https://doi.org/10.1002/hyp.10360, 2015.
Marx, A., Kumar, R., Thober, S., Rakovec, O., Wanders, N., Zink, M., Wood, E.
F., Pan, M., Sheffield, J., and Samaniego, L.: Climate change alters low
flows in Europe under global warming of 1.5, 2, and 3 ∘C, Hydrol.
Earth Syst. Sci., 22, 1017–1032, https://doi.org/10.5194/hess-22-1017-2018,
2018.
McKee, T. B., Doesken, N. J., and Kleist, J.: The relationship of drought
frequency and duration to time scales, 8th Conference on Applied Climatology.
American meteorological society, Boston, USA, 1993.
Mosley, M. P.: Regional differences in the effects of El Nino and La Nina on
low flows and floods, Hydrolog. Sci. J., 45, 249–267,
https://doi.org/10.1080/02626660009492323, 2000.
NLWKN: Globaler Klimawandel: Wasserwirtschaftliche Folgenabschätzung
für das Binnenland, Oberirdische Gewässer Band 33,
Niedersächsischer Landesbetrieb für Wasserwirtschaft, Küsten- und
Naturschutz, Betriebsstelle Hannover-Hildesheim, 2012.
NLWKN: Globaler Klimawandel: Wasserwirtschaftliche Folgenabschätzung
für das Binnenland, Oberirdische Gewässer Band 41,
Niedersächsischer Landesbetrieb für Wasserwirtschaft, Küsten- und
Naturschutz, Betriebsstelle Hannover-Hildesheim, 2017.
Osuch, M., Romanowicz, R., and Wong, W. K.: Analysis of low flow indices
under varying climatic conditions in Poland, Hydrol. Res. 49, 373–389,
https://doi.org/10.2166/nh.2017.021, 2017.
Pettitt, A. N.: A non-parametric approach to the change-point problem, Appl.
Stat.-J. Roy. St. C., 28, 126–135, https://doi.org/10.2307/2346729, 1979.
Roudier, P., Andersson, J. C. M., Donnelly, C., Feyen, L., Greuell, W., and
Ludwig, F.: Projections of future floods and hydrological droughts in Europe
under a +2 ∘C global warming, Climatic Change, 135, 341–355,
https://doi.org/10.1007/s10584-015-1570-4, 2016.
Schneider, C., Laizé, C. L. R., Acreman, M. C., and Flörke, M.: How will
climate change modify river flow regimes in Europe?, Hydrol. Earth Syst.
Sci., 17, 325–339, https://doi.org/10.5194/hess-17-325-2013, 2013.
Schwarz, G.: Estimating Dimension of a Model, Ann. Stat., 6, 461–464,
https://doi.org/10.1214/aos/1176344136, 1978.
Van Loon, A. F. and Laaha, G.: Hydrological drought severity explained by
climate and catchment characteristics, J. Hydrol., 526, 3–14,
https://doi.org/10.1016/j.jhydrol.2014.10.059, 2015.
van Vliet, M. T. H., Donnelly, C., Strömbäck, L., Capell, R., and Ludwig,
F.: European scale climate information services for water use sectors, J.
Hydrol., 528, 503–513, https://doi.org/10.1016/j.jhydrol.2015.06.060, 2015.
Vicente-Serrano, S. M., Begueria, S., and Lopez-Moreno, J. I.: A Multiscalar
Drought Index Sensitive to Global Warming: The Standardized Precipitation
Evapotranspiration Index, J. Climate, 23, 1696–1718,
https://doi.org/10.1175/2009JCLI2909.1, 2010.
Vrugt, J. A. and Robinson, B. A.: Improved evolutionary optimization from
genetically adaptive multimethod search, P. Natl. Acad. Sci. USA, 104,
708–711, https://doi.org/10.1073/pnas.0610471104, 2007.
Wallner, M., Haberlandt, U., and Dietrich, J.: A one-step similarity approach
for the regionalization of hydrological model parameters based on
Self-Organizing Maps, J. Hydrol., 494, 59–71,
https://doi.org/10.1016/j.jhydrol.2013.04.022, 2013.
Wanders, N. and Van Lanen, H. A. J.: Future discharge drought across climate
regions around the world modelled with a synthetic hydrological modelling
approach forced by three general circulation models, Nat. Hazards Earth Syst.
Sci., 15, 487–504, https://doi.org/10.5194/nhess-15-487-2015, 2015.
Wanders, N. and Wada, Y.: Human and climate impacts on the 21st century
hydrological drought, J. Hydrol., 526, 208–220,
https://doi.org/10.1016/j.jhydrol.2014.10.047, 2015.
Wilby, R. L. and Harris, I.: A framework for assessing uncertainties in
climate change impacts, Low-flow scenarios for the River Thames, UK, Water
Resour. Res., 42, W02419, https://doi.org/10.1029/2005wr004065, 2006.
Wilcoxon, F.: Individual Comparisons by Ranking Methods, Biometrics Bull., 1,
80–83, https://doi.org/10.2307/3001968, 1945.
Short summary
Low-flow events are little dynamic in space and time. Thus, it is hypothesized that models can be found, based on simple statistical relationships between low-flow metrics and meteorological states, that can help identify potential low-flow drivers. In this study we assess whether such relationships exist and whether they can be applied to predict future low flow within regional climate change impact assessment in the northwestern part of Germany.
Low-flow events are little dynamic in space and time. Thus, it is hypothesized that models can...
